Efficiently Learning a Detection Cascade with Sparse Eigenvectors

Simple item page
dc.creatorShen, Chunhua
dc.creatorPaisitkriangkrai, Sakrapee
dc.creatorZhang, Jian
dc.date2009-03-18
dc.date.accessioned2026-07-07T12:53:35Z
dc.date.available2026-07-07T12:53:35Z
dc.descriptionIn this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce Greedy Sparse Linear Discriminant Analysis (GSLDA) \cite{Moghaddam2007Fast} for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with \cite{Viola2004Robust}. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA.
dc.description12 pages, conference version published in CVPR2009
dc.identifierhttps://arxiv.org/abs/0903.3103
dc.identifierhttp://arxiv.org/abs/0903.3103
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/223667
dc.subjectMultimedia
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.titleEfficiently Learning a Detection Cascade with Sparse Eigenvectors
dc.typetext

Files

Collections

arXiv